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IEEE International Conference on Robotics and Biomimetics

作者： Le Chen Jun Zhao Weishi Li Yuliang Zhao Detachment 60 PLA Unit 92493 Huludao China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China

ISBN: (纸本)9781665481106

Sea ice seriously endangers the safe navigation of ships at sea. Therefore, it is the key to accurately judge the type of sea ice around the ship. However, remote sensing technology will be restricted by external weather changes. Therefore, this paper presents a method based on statistical feature extraction and machine learning of ship main engine vibration signal, so as to realize the recognition of sea ice types. Firstly, according to the sea ice video information captured by the camera, the acceleration and vibration data collected by the vibration sensor are divided, and the wavelet threshold denoising algorithm is adopted to enhance the features. Then, based on the statistical characteristics of vibration signals, time domain and frequency domain features are extracted. Finally, Random Forest(RF) and Gradient Boosting Decision Tree(GBDT) machine learning algorithms are used to classify the extracted eigenvalues. The experimental results show that the accuracy of sea ice identification using ship main engine vibration signals is 79%. This method provides a reference for identifying sea ice types by ship ice-induced vibration, and is helpful for further research on sea ice classification.

关键词： Vibrations Machine learning algorithms Navigation Noise reduction Data acquisition Feature extraction Robot sensing systems

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Hybrid approach for speaker recognition based on formant and pitch extraction 22

Hybrid approach for speaker recognition based on formant and...

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22nd International Conference on Cyberworlds, CW 2023

作者： Boujnah, Sana Ferjaoui, Radhia Khalifa, Anouar Ben University of Tunis El Manar Tunisia National Engineering School of Tunis Tunisia NOCCS-Laboratory of Networked Objects Control and Communication Systems Eniso Tunisia Istmt Tunis Tunisia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef7100 Tunisia

ISBN: (纸本)9798350315653

Human voice is an ideal data source for identifying people in many applications. Because of the increasing need for security in different public places, voice biometrics may be a good solution, as we can easily take voice records. This paper provides a brief overview of the approaches utilized in recognizing speakers, and then presents a novel approach for recognizing speakers in degraded smart-home conditions. The suggested approach includes a pre-processing phase, a feature extraction phase, and a classification phase, where the feature extraction phase consists of formant extraction to get the spectrum energy maxima of speech audio, dynamic time warping (DTW)to find an optimal alignment between two provided temporal sequences under definite restrictions, and refinement process to improve the results of the DTW system output. The experiments are carried out on a database containing 1,248 samples in order to validate the suggested approach. The latter has good results as regards the state of the art with 94.5% accuracy. © 2023 IEEE.

关键词： Feature extraction

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Thai Beef Cattle Recognition for Fattening Farms: An Empirical Study of Cattle Cooperative Practices in Chiang Rai

Thai Beef Cattle Recognition for Fattening Farms: An Empiric...

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International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering (ECTI-NCON)

作者： Ant Maw Khon Myint Myat Pradorn Sureephong Qing Fan Santichai Wicha School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand Key Laboratory of China(Guangxi)-ASEAN CrossBorder Animal Disease Prevention and Control Ministry of Agriculture and Rural Affairs of China Guangxi Veterinary Research Institute Guangxi China Computer and Communication Engineering for Capacity Building (CCC) School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This research focuses on developing an automated system to identify the breed and sex of Thai beef cattle using advanced computer vision technology. By using the YOLOv11 (You Only Look Once) model, a state-of-the-art object detection tool, this system can quickly and accurately identify cattle breeds and determine their sex. High-resolution image datasets were collected and augmented, yielding 81.2% accuracy for breed detection and 95.0% for sex classification, with mAP@50 scores of 85.1% and 99.3%, respectively. However, limited data for certain breeds, like Thai Native and Japanese cattle, reduced the model’s performance for those categories. To overcome this, we aim to expand the dataset from CCTV footage in future studies. The system will include user-friendly features, such as real-time alerts and dashboards, making it practical for farmers to use. This project demonstrates how AI can improve cattle farming by saving time, reducing errors, and supporting better decisions. Future improvements will explore applications in disease detection and behavior monitoring.

关键词： YOLO Deep learning Computer vision Cows Real-time systems Data models Object recognition Monitoring Farming Diseases

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Person Identification with Voice and Ear-print in Degraded Conditions for Smart Home Access 22

Person Identification with Voice and Ear-print in Degraded C...

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22nd International Conference on Cyberworlds, CW 2023

作者： Boujnah, Sana Ferjaoui, Radhia Khalifa, Anouar Ben University of Tunis El Manar National Engineering School of Tunis Tunisia NOCCS-Laboratory of Networked Objects Control and Communication Systems Eniso Tunisia Istmt Tunis Tunisia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef7100 Tunisia

ISBN: (纸本)9798350315653

Since the smartphone is adapted to the ambient intelligence and the smart home systems are remotely accessed through the smartphones, there is a need for a secure authentication system based on some biometrics proprieties that can be taken from a smartphone. The identification of persons through ear and voice print is one of the basic biometric matters. The earlier research in ear recognition have shown that human ear is one of the representative human biometrics with uniqueness and stability. Indeed, the human voice is a perfect source of data for person identification in many applications. In this paper, we propose a fusion between the ear and voice biometrics in degraded conditions in a smart home context at 3 levels (feature, score, and decision). The experiments are conducted on the EVDDC database and a chimeric database (TIMIT and USTB-I). The best results are obtained with the feature level fusion (95.8%) with the KNN classifier. © 2023 IEEE.

关键词： Biometrics

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GPIO Based Iterative Learning control for Manipulators with Time-Varying Disturbances

GPIO Based Iterative Learning Control for Manipulators with ...

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Chinese control Conference (CCC)

作者： Huangyuwei Lu Zuo Wang Jiankun Sun Xi Xiao Shihua Li School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex systems of Engineering Ministry of Education Nanjing China Shenzhen Research Institute Southeast University Shenzhen China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing China

To improve the disturbance rejection ability and repetitive tracking accuracy of manipulators, a composite iterative learning control (ILC) scheme via generalized proportional integral observer (GPIO) is proposed. A high-order polynomial is first employed to model the time-varying disturbances that include unmodeled dynamics, parameter uncertainties and load fluctuations. A GPIO is then designed to estimate the disturbances and high-order time derivatives. By introducing estimations into a PD-type ILC at each iteration, a composite control scheme is obtained, such that the undesirable influence of time-varying disturbances can be effectively attenuated in the repetitive tracking process of manipulators. In this paper, the rigorous stability analysis of the closed-loop system is presented to guarantee that the tracking error exponentially converges to zero as the iteration number tends to infinity. The effectiveness of the proposed control scheme is finally verified by simulation results.

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Multimodal MR Brain Image Segmentation using Hybrid Optimization Techniques for Earlier Prognosis of Tumor

Multimodal MR Brain Image Segmentation using Hybrid Optimiza...

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Advanced Computing and communication Technologies for High Performance Applications

作者： Baiju Karun T. Arun Prasath M. Pallikonda Rajasekaran M Rakhee Department of Instrumentation and Control Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Biomedical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Computer Science & Engineering School of Engineering Cochin University of Science and Technology Kerala India

Segmentation of medical images is the process of dividing an image into regions that have some characteristics that are homogeneous and consistent. Medical image segmentation for brain images is an important process in the early diagnosis of brain tumors, brain abnormalities, and their treatment planning. When performed by medical specialists, segmentation of Magnetic Resonance Imaging (MRI) takes a long time. This paper needs to propose some new hybrid approaches for image segmentation and classification for early detection and treatment of brain tumors. The conventional fuzzy c-means clustering (FCM) method is susceptible to artifacts; the local spatial information is frequently presented to an objective function to enhance the FCM method's reliability for segmenting the images. To overcome this limitation, the authors proposed the Fast and Robust FCM (FRFCM), an optimized FCM technique that utilizes morphological restoration (MR) and membership filtration which is considerably quicker and much more reliable than classical FCM. Following the clustering procedure, the new Social Spider Optimization (SSO) swarm algorithm relies on the co - operative features of the social spider is employed to obtain the best outcome. The novel integration of FRFCM and SSO has been affirmed through evaluating on numerous different BraTS datasets, yielding improved performance metric values.

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Wireless recharging sensor networks cross-layer optimization based on successive interference cancellation

Wireless recharging sensor networks cross-layer optimization...

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作者： Xu, Juan Xu, Xingxin Ding, Xu Shi, Lei Lu, Yang School of Computer Science and Information Engineering HeFei University of Technology Hefei China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei China

In wireless sensor networks (WSN), communication interference and the energy limitation of sensor nodes seriously hamper the network performance such as throughput and network lifetime. In this paper, we focus on the Successive Interference Cancellation (SIC) and Wireless Energy Transmission (WET) technology aiming to design a heuristic power control algorithm and an efficient cross-layer strategy to realize concurrency communication and improve the network throughput, channel utilization ratio and network lifetime. We realize that the challenge of this problem is that joint consideration of communication interference and energy shortage makes the problem model more complicated. To solve the problem efficiently, we adopt link scheduling strategy, time-slice scheduling scheme and energy consumption optimization protocol to construct a cross-layer optimization problem, then use an approximate linearization method to transform it into a linear problem which yields identical optimal value and solve it to obtain the optimal work strategy of wireless charging equipment (WCE). Simulation results show that adopting SIC and WCE can greatly improve communication capability and channel utilization ratio, and increase throughput by 200% to 500% while prolonging the network lifetime. © 2020 The Institute of Electronics, Information and communication Engineers

关键词： Power control

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WcAMDG: Wireless Charging and Assisted Mobile Data Gathering Algorithm in WRSNs 14th

WcAMDG: Wireless Charging and Assisted Mobile Data Gatherin...

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14th China Conference on Wireless Sensor Networks, CWSN 2020

作者： Lyu, Zengwei Wei, Zhenchun Wang, Xinchen Lu, Yang Xu, Guowei School of Computer Science and Information Engineering Hefei University of Technology Hefei China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei China

ISBN: (纸本)9789813342132

In Wireless Sensor Networks, sensor nodes near the base station need to forward data generated by the other nodes, which causes the early death of these sensor nodes. To solve the problem, some researchers used mobile sink to collect data from sensor nodes to save energy, some researchers employed mobile wireless charger to replenish energy for sensor nodes. In this paper, a Mobile Equipment (ME) with wireless charging and data collecting function is employed due to the different frequencies of data transmission and energy transfer, therefore it can save energy and replenish energy for the network simultaneously. An energy-saving coefficient is designed to balance the multi-hop transmission and the data collecting by the ME in each sensor node. Based on Lagrange Dual Decomposition and Sub-gradient Optimization methods, a distributed on-demand charging planning algorithm with assisted data collecting is proposed to optimize the charging energy in each sensor node and the routing of the WSNs. Experiments show that our proposed algorithm can effectively prolong the lifespan of network, balance the energy consumption of sensor nodes, and improve the energy utilization of the ME. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Sensor nodes

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OFDM Signal Design Based on Electrode-based Through-the-earth communication

OFDM Signal Design Based on Electrode-based Through-the-eart...

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International Conference on Ubiquitous Computing and communications and International Symposium on Cyberspace and Security (IUCC-CSS)

作者： Gan Zhang Zhan Xu Jinhui Chen Xiaowen Wen Key Laboratory of Modern Measurement and Control Technology Beijing Information Science and Technology University Beijing China School of Automation Beijing Information Science and Technology University Beijing China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781665466684

Through-the-earth (TTE) communication is of great significance to establish the information transmission with the trapped people at the first time and ensure the smooth flow of information. The complex electromagnetic environment of the terrestrial channel brings many difficulties in wireless signal transmission. This paper proposes an orthogonal frequency division multiplexing (OFDM) signal design scheme of electrode-based TTE communication. The scheme, which has intense penetration and high reliability, designs the frequency and modulation mode of the subcarrier according to the electromagnetic characteristics of earth channel and noise interference. This paper also provides the results of field experiments to show the performance of this OFDM signal.

关键词： Earth Wireless communication Frequency modulation OFDM Low-pass filters Interference Ubiquitous computing

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Conditional Adversarial Domain Adaption based on Self-attention

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第1期7卷 175-188页

作者： Zhao, Li-Quan Zhang, Yu-Peng Teng, Zi-Ming Jiang, Zi-Cong Cui, Ying Kan, Zhong-Feng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China College of information and communication Jilin University Changchun130012 China Graduate school of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan Zhuhai Power Supply Bureau Guangdong Electric Power Corporation Zhuhai519000 China Jilin Power Supply Bureau Jilin Electric Power Corporation Jilin132012 China

Transfer learning is a deep learning method. It transforms knowledge in the source domain and feeds it to the target domain to improve the performance of the target domain model. The conditional adversarial domain adaption method is one of a number of transfer learning methods. It uses Resnet as the feature extraction network;however, Resnet can only determine the relationship between local features, and so its feature extraction accuracy is limited. To overcome this problem, a self-attention layer has been designed in order to determine the relationship among all features. This layer is added between the fourth layer and fifth layer of Resnet. Besides, a cross-entropy function is introduced into loss function, and a sigmoid layer is designed and used as the out layer in conditional adversarial domain adaption. These produce gradient disappearance in the deep network, and influence the discriminator training effect. To solve that problem, a least squares function is used as to design the loss function. The new type of loss function does not require the sigmoid function. Using theoretical analysis, we explain how this new loss function can be used to solve the gradient disappearance problem, thus rendering the network structure more stable. We compare our proposed method with other datasets, including Joint Adaptation Networks, adversarial domain adaptation, conditional adversarial domain adaption, Resnet-50, domain-adversarial neural network on imageCLEF-DA, Office-Home, and Office-31. Our simulation results show that the proposed conditional adversarial domain adaption based on self-attention method has the fastest convergence speed and highest accuracy the among different datasets. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Computer vision

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